Sunday, March 18, 2012

Hedging by Gold Mining Companies


Hedging by Gold Mining Companies

It is natural for a gold mining company to consider hedging against prices of gold. Typically it takes several years to extract all the gold from a mine. Once a gold mining company decides to go ahead with production at a particular time, it has a big exposure to the price of gold. Indeed a mine that looks profitable at the outset could become unprofitable if the prices of gold plunges.

Gold mining companies are careful to explain their hedging strategies to potential shareholders. Some gold mining companies do not hedge. They tend to attract shareholders who buy gold stock because they want to benefit when the price of gold increases and are prepared to accept the risk of a loss from a decrease in the price of gold. Other companies choose to hedge. They estimated the number of ounces of gold they will produce each month for the next few years and enter into short futures or forward contracts to lock in the price for all or part of this.

Suppose you are Goldman Sachs and are approached by a gold mining company that wants to sell you a large amount of gold in 1 year at fixed price. How do you set the price and then hedge your risk? The answer is that you can hedge by borrowing the gold from a central bank, selling it immediately in the spot market, and investing the proceeds at the risk-free rate. At the end of the year, you buy the gold from the gold mining company and use it to repay the central bank. The fixed forward price you set for the gold reflects the risk-free rate you earn and the lease rate you pay the central bank for borrowing gold.
-         Sujit Kapadia

Wednesday, March 7, 2012

BRINGS A SMILE EVEN IN A BLEAK MARKET: Subprime Mortgage Crisis

BRINGS A SMILE EVEN IN A BLEAK MARKET: Subprime Mortgage Crisis: Subprime Mortgage Crisis What is Subprime lending? Subprime lending is making the loans available to the people .where the interest ra...

Thursday, March 1, 2012

A BAD DAY on WALL STREET – BLACK MONDAY

A BAD DAY on WALL STREET – BLACK MONDAY
On a typical day the overall value of stocks traded on the U.S. stock market can rise or fall by 1% or even more. This is a lot, but nothing compared to what happened on Monday, October 19, 1987. On “Black Monday,” the Dow Jones Industrial Average (an average of 30 large industrial stocks) fell by 25.6%! From January 1, 1980, to October 16, 1987, the standard deviation of daily percentage price changes on Dow Jones was 1.16%, so the drop of 25.6% was a negative return of 25.6/1.16=22 standard deviation. The enormity of this drop can be seen in the figure below.

 
If the daily percentage price changes are normally distributed, then the probability of a drop of at least 22 standard deviation is Pr(Z less than or equal to -22). We can calculate this with help of computer, this probability is 1.4*10^-107, that is, 0.000....000014, where there are total of 106 zeros.
So, how small is 1.4*10^-107? Consider the following which are larger than this number:
·        The world population is about 6 billion, so the probability of winning a random lottery among all the living people by you is one in 6 billion i.e. 2*10^-10
·        The universe is believed to have existed for 15 billion years, or about 5*10^17 seconds, so the probability of choosing a particular second at random from all the seconds since the beginning of time is 2*10^-18.
·        There are approximately 10^43 molecules of gas in the first kilometre above the earth’s surface. The probability of choosing one at a random is 10^43.
Although Wall Street did have a bad day, the fact that is happened at all suggests that its probability was more than 1.4*10^-107. In fact, stock price percentage changes have a distribution with the heavier tails than the normal distribution; in the other words, there are more days with large positive or negative changes than the normal distribution would suggest. For this reason, finance professional use econometric models in which the variance of the percentage change in stock prices can evolve over time, so some periods have higher volatility than other. These models with changing variances are more consistent with the very bad – and very good – day we actually see on Wall Street.